Frequent Itemset Mining Algorithm Research Articles

Improvisation in Efficiency of Apriori Algorithm for Mining Frequent Itemsets

Improvisation in Efficiency of Apriori Algorithm for Mining Frequent Itemsets

A Survey and comparative study of the various algorithms for Frequent Itemset Mining

A Survey and comparative study of the various algorithms for Frequent Itemset Mining

Postdiffset: an Eclat-like algorithm for frequent itemset mining

Frequent itemset mining is a major field in data mining techniques. This is because it deals with usual and normal occurrences of set of items in a database transaction. Originated from market basket analysis, frequent itemset generation may lead to the formulation of association rule as to derive correlation or patterns. Association rule mining still remains as one of the most prominent areas in data mining that aims to extract interesting correlations, frequent patterns, association or casual structures among set of items in the transaction databases. Underlying structure of association rules mining algorithms are based upon horizontal or vertical data formats. These two data formats have been widely discussed by showing few examples of algorithm of each data formats. The works on horizontal approaches suffer in many candidate generation and multiple database scans that contributes to higher memory consumptions. In response to improve on horizontal approach, the works on vertical approaches are established. Eclat algorithm is one example of algorithm in vertical approach database format. Motivated to its ‘fast intersection’, in this paper, we review and analyze the fundamental Eclat and Eclat-variants such as tidset, diffset, and sortdiffset. In response to vertical data format and as a continuity to Eclat extension, we propose a postdiffset algorithm as a new member in Eclat variants that use tidset format in the first looping and diffset in the later looping. We present the performance of postdiffset results in time execution as to indicate some improvements has been achieved in frequent itemset mining.

A haoop-based parallel mining of frequent itemsets using N-Lists

Frequent pattern mining is the most important phase of association rule mining process because of its time and space complexity. Several methods have attempted to improve the performance of association rule mining by enhancing frequent pattern mining efficiency. Due to the large size of the data-sets and huge amounts of data which should be mined, many parallel and distributed mining approaches have been introduced to divide data-sets or to distribute mining processes between multiple processors or computers and thus, improve the efficiency of the mining process. In this paper, we propose a hadoop-based parallel implementation of PrePost+ algorithm for frequent itemset mining. In our parallel approach, the process of constructing N-Lists of itemsets has been distributed between the mappers and the operation of the final pruning process and extracting frequent itemsets has been carried out by reducers in a map-reduce parallel programming model. The experimental results show that our hadoop-based PrePost+(HBPrePost+) algorithm outperforms one of the best existing parallel methods of frequent itemset mining (PARMA) in terms of execution time.

NegFIN: An efficient algorithm for fast mining frequent itemsets

Frequent itemset mining is a basic data mining task and has numerous applications in other data mining tasks. In recent years, some data structures based on sets of nodes in a prefix tree have been presented. These data structures store essential information about frequent itemsets. In this paper, we propose another efficient data structure, NegNodeset. Similar to other such data structures, the basis of NegNodeset is sets of nodes in a prefix tree. NegNodeset employs a novel encoding model for nodes in a prefix tree based on the bitmap representation of sets. Based on the NegNodeset data structure, we propose negFIN, which is an efficient algorithm for frequent itemset mining. The efficiency of the negFIN algorithm is confirmed by the following three reasons: (1) the NegNodesets of itemsets are extracted using bitwise operators, (2) the complexity of calculating NegNodesets and counting supports is reduced to O(n), where n is the cardinality of NegNodeset, and (3) it employs a set-enumeration tree to generate frequent itemsets and uses a promotion method to prune the search space in this tree. Our extensive performance study on a variety of benchmark datasets indicates that negFIN is the fastest algorithm, compared with previous state-of-the-art algorithms. However, our algorithm runs with the same speed as dFIN on some datasets.

A frequent itemset mining algorithm based on composite granular computing

This paper proposes a frequent itemset mining algorithm based on the divide and conquer strategy in composite granular computing. In order to construct composite information granules (CIGs) and find the frequent patterns, an iterative approach is used in this algorithm. First, create atomic informa tion granules. Next, atomic composite information granules are generated by atomic information granules. Then, through the intersect operation between atomic composite information granules and prune action, the frequent 2-CIGs that will be used to construct frequent 3-CIGs will be constructed, and so on, until no more frequent CIGs can be found. When creating CIGs, this method will improve the computing speed by logical operation in binary. It can avoid scanning database frequently and avoid using complex data structure, so it will reduce the I/O overhead and save a lot of memory space. And it also can optimize the generation of candidate CIGs and compress the transaction database dynamically. The experimental results show that this algorithm has good performance and has low computational complexity and high efficiency.

Multi-level dataset decomposition for parallel frequent itemset mining on a cluster of personal computers

Frequent Itemset mining is time consuming for large datasets. Many parallel frequent itemset mining algorithms have been proposed to speed up the mining process. This paper presents a parallel frequent itemset mining algorithm on a cluster of personal computers. To facilitate parallel frequent itemset mining, we use prefix path based method to decompose a transactional dataset into its frequent 1-itemset sub-datasets. We called the parallel frequent itemset mining algorithm based on the frequent 1-itemset sub-dataset decomposition the single-level parallel frequent itemset mining algorithm (SLPFIM) in our PC cluster platform. To mitigate the bottleneck caused by time-consuming 1-itemset sub-datasets, we propose a multi-level parallel frequent itemset mining (MLPFIM) algorithm to further decompose the time-consuming 1-itemset sub-datasets into their corresponding sub-sub-datasets. The fine granule of the sub-sub-datasets enhances the load balancing in parallel frequent itemset mining. The experimental results showed that the SLPFIM offered a maximum of 11.9x speedup over the non-parallel execution of the FP-Growth algorithm while the MLPFIM achieved a maximum of 23.1x speedup over the non-parallel execution of the FP-Growth algorithm. The experimental results also showed that the MLPFIM offered a maximum of 2.14x speedup over the SLPFIM.

Geo Map Visualization for Frequent Purchaser in Online Shopping Database Using an Algorithm LP-Growth for Mining Closed Frequent Itemsets

Numerous frequent itemsets are explored using frequent itemset mining algorithm which contains redundant information. Fortunately, this issue is decreased to the mining of closed frequent itemsets. However, these approaches still have some performance bottlenecks like processing time and storage space. Moreover, new algorithms of closed frequent itemsets are presented. In this paper, the proposed closed frequent itemset (LP-Closed-tree) using Linear prefix growth method is introduced which is a powerful approach for mining frequent itemsets. It builds basic tree and mines frequent itemsets. The proposed LP-Closed-tree is implemented on real and dense database like online shopping database and chess and is evaluated with other existing algorithms. While using online shopping dataset, the frequent purchaser of the dataset is visualized using google map in geographical method. After comprehensive empirical appraisal it is found that the proposed LP-Closed-tree algorithms are faster in many cases. Moreover, these algorithms are known for relatively lesser consumption of time and memory in cases of large and dense database.

A weighted frequent itemset mining algorithm for intelligent decision in smart systems

Intelligent decision is the key technology of smart systems. Data mining technology has been playing an increasingly important role in decision-making activities. Frequent itemset mining (FIM), as an important step of association rule analysis, is becoming one of the most important research fields in data mining. Weighted FIM in uncertain databases should take both existential probability and importance of items into account in order to find frequent itemsets of great importance to users. However, the introduction of weight makes the weighted frequent itemsets not satisfy the downward closure property any longer. As a result, the search space of frequent itemsets cannot be narrowed according to downward closure property which leads to a poor time efficiency. In this paper, the weight judgment downward closure property for the weighted frequent itemsets and the existence property of weighted frequent subsets are introduced and proved first. Based on these two properties, the Weight judgment downward closure property-based FIM (WD-FIM) algorithm is proposed to narrow the searching space of the weighted frequent itemsets and improve the time efficiency. Moreover, the completeness and time efficiency of WD-FIM algorithm are analyzed theoretically. Finally, the performance of the proposed WD-FIM algorithm is verified on both synthetic and real-life data sets.

Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data

Frequent itemsets mining with differential privacy refers to the problem of mining all frequent itemsets whose supports are above a given threshold in a given transactional dataset, with the constraint that the mined results should not break the privacy of any single transaction. Current solutions for this problem cannot well balance efficiency, privacy, and data utility over large-scale data. Toward this end, we propose an efficient, differential private frequent itemsets mining algorithm over large-scale data. Based on the ideas of sampling and transaction truncation using length constraints, our algorithm reduces the computation intensity, reduces mining sensitivity, and thus improves data utility given a fixed privacy budget. Experimental results show that our algorithm achieves better performance than prior approaches on multiple datasets.

A Summary of Research on Frequent Itemsets Mining Technology

In recent years, communication technology and Internet technology are developing rapidly. In this context, people pay more and more attention to the importance of data, especially the implied information with timeliness and value, and accessing to such information relies on data mining. Data mining technology has many important branches. In recent years, data mining has been widely concerned about the association rules data mining technology is one of them, and its biggest advantage is the ability to derive valuable, but less significant, patterns from a large amount of information. The key and core problem of association rule data mining is frequent itemsets mining. Based on the research status of frequent itemsets mining technology, the research results and basic ideas of frequent itemsets mining algorithms are discussed and analyzed.

A novel Bit Vector Product algorithm for mining frequent itemsets from large datasets using MapReduce framework

Frequent itemset mining (FIM) is an interesting sub-area of research in the field of Data Mining. With the increase in the size of datasets, conventional FIM algorithms are not suitable and efforts are made to migrate to the Big Data Frameworks for designing algorithms using MapReduce like computing paradigms. We too interested in designing MapReduce based algorithm. Initially, our Parallel Compression algorithm makes data simpler to handle. A novel bit vector data structure is proposed to maintain compressed transactions and it is formed by scanning the dataset only once. Our Bit Vector Product algorithm follows the MapReduce approach and effectively searches for frequent itemsets from a given list of transactions. The experimental results are present to prove the efficacy of our approach over some of the recent works.

Diagnosis of coronary artery disease using an efficient hash table based closed frequent itemsets mining.

This paper proposes an efficient hash table based closed frequent itemsets (HCFI) mining algorithm to envisage coronary artery disease early. HCFI algorithm generates closed frequent itemsets efficiently by performing intersection operation on transaction id's of itemset without considering the name of item/itemset. The employed hash table reduces search efficiency to O(1) or constant time. HCFI algorithm is applied on the UCI (University of California, Irvine) Cleveland dataset, a biological database of cardiovascular disease to generate closed frequent itemsets on the dataset. The findings of HCFI algorithm are (1) it determines a set of distinguished features to differentiate a 'healthy' and a 'sick' class. The features such as heart status being normal, oldpeak being less than or equal to 1.2, slope being up, number of vessels colored being zero, absence of exercise-induced angina, maximum heart rate achieved between 151 and 180 are referred as 'healthy' class. The features like chest pain are being asymptomatic, heart-status being reversible defect, slope being flat, and presence of exercise-induced-angina and serum cholesterol being greater than 240 indicate a presumption of heart disease to both genders. (2) It predicts that females have less chance of coronary heart disease than males. This algorithm is also compared with two other state-of-the-art-algorithms 'NAFCP' (N-list based algorithm for mining frequent closed patterns) and 'PredictiveApriori' to show the effectiveness of the proposed algorithm.

A Novel UML Based Approach for Early Detection of Change Prone Classes

This article describes how predicting change-prone classes is essential for effective development of software. Evaluating changes from one release of software to the next can enhance software quality. This article proposes an efficient novel-based approach for predicting changes early in the object-oriented software. Earlier researchers have calculated change prone classes using static characteristics such as source line of code e.g. added, deleted and modified. This research work proposes to use dynamic metrics such as execution duration, run time information, regularity, class dependency and popularity for predicting change prone classes. Execution duration and run time information are evaluated directly from the software. Class dependency is obtained from UML2.0 class and sequence diagrams. Regularity and popularity is acquired from frequent item set mining algorithms and an ABC algorithm. For classifying the class as change-prone or non-change-prone class an Interactive Dichotomizer version 3 (ID3) algorithm is used. Further validation of the results is done using two open source software, OpenClinic and OpenHospital.

Data Mining Itemset of Big Data Using Pre-Processing Based on Mapreduce FrameWork with ETL Tools

As data volumes continue to grow, they quickly consume the capacity of data warehouses and application databases. Is your IT organization forced into costly upgrades to expensive databases and data warehouse hardware appliances and enormous amount of data is getting explored through Internet of Things (IoT) as technologies are advancing and people uses these technologies in day to day activities, this data is termed as Big Data having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets but it has large communication cost which reduces execution efficiency. This proposed new pre-processed k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using k-means algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets from generated clusters using MapReduce programming model. Results shown that execution efficiency of ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as one of the pre-processing technique.

Parallel State of the Art Algorithms for Frequent Itemset Mining – A Concise Descriptive Summary of Scalable Approaches

Frequent Item set Mining is a Data Mining task that has attracted the researchers’ interests in a way that very few other tasks have done. This concept is generally used in Decision Support problems. Many serial and parallel algorithms have been developed for Frequent Item set Mining. In this paper, we have focused on the developments of parallel algorithms in this area so far. We start with an Apriori-based parallel algorithm that focuses on minimizing the communication overhead even if, in parallel, it requires redundant duplicate computations. Then we discuss an algorithm that utilizes the system memory in amore effective manner. Then we give an account of the algorithm that reduces the synchronization between the processors, segments the database, and integrates load balancing. Then we describe an algorithm that partitions the Candidate Item sets intelligently. Finally, we give an account of an algorithm that combines two famous algorithms to leverage their cumulative advantage. While keeping the original ideas intact, we have avoided the cumbersome notations to keep it easily intelligible.

Efficient and Versatile FPGA Acceleration of Support Counting for Stream Mining of Sequences and Frequent Itemsets

Stream processing has become extremely popular for analyzing huge volumes of data for a variety of applications, including IoT, social networks, retail, and software logs analysis. Streams of data are produced continuously and are mined to extract patterns characterizing the data. A class of data mining algorithm, called generate-and-test , produces a set of candidate patterns that are then evaluated over data. The main challenges of these algorithms are to achieve high throughput, low latency, and reduced power consumption. In this article, we present a novel power-efficient, fast, and versatile hardware architecture whose objective is to monitor a set of target patterns to maintain their frequency over a stream of data. This accelerator can be used to accelerate data-mining algorithms, including itemsets and sequences mining. The massive fine-grain reconfiguration capability of field-programmable gate array (FPGA) technologies is ideal to implement the high number of pattern-detection units needed for these intensive data-mining applications. We have thus designed and implemented an IP that features high-density FPGA occupation and high working frequency. We provide detailed description of the IP internal micro-architecture and its actual implementation and optimization for the targeted FPGA resources. We validate our architecture by developing a co-designed implementation of the Apriori Frequent Itemset Mining (FIM) algorithm, and perform numerous experiments against existing hardware and software solutions. We demonstrate that FIM hardware acceleration is particularly efficient for large and low-density datasets (i.e., long-tailed datasets). Our IP reaches a data throughput of 250 million items/s and monitors up to 11.6k patterns simultaneously, on a prototyping board that overall consumes 24W in the worst case. Furthermore, our hardware accelerator remains generic and can be integrated to other generate and test algorithms.

Parallel Incremental Frequent Itemset Mining for Large Data

Frequent itemset mining (FIM) is a popular data mining issue adopted in many fields, such as commodity recommendation in the retail industry, log analysis in web searching, and query recommendation (or related search). A large number of FIM algorithms have been proposed to obtain better performance, including parallelized algorithms for processing large data volumes. Besides, incremental FIM algorithms are also proposed to deal with incremental database updates. However, most of these incremental algorithms have low parallelism, causing low efficiency on huge databases. This paper presents two parallel incremental FIM algorithms called IncMiningPFP and IncBuildingPFP, implemented on the MapReduce framework. IncMiningPFP preserves the FP-tree mining results of the original pass, and utilizes them for incremental calculations. In particular, we propose a method to generate a partial FP-tree in the incremental pass, in order to avoid unnecessary mining work. Further, some of the incremental parallel tasks can be omitted when the inserted transactions include fewer items. IncbuildingPFP preserves the CanTrees built in the original pass, and then adds new transactions to them during the incremental passes. Our experimental results show that IncMiningPFP can achieve significant speedup over PFP (Parallel FPGrowth) and a sequential incremental algorithm (CanTree) in most cases of incremental input database, and in other cases IncBuildingPFP can achieve it.

A Matrix based Maximal Frequent Itemset Mining Algorithm without Subset Creation

A Matrix based Maximal Frequent Itemset Mining Algorithm without Subset Creation

HFIM: a Spark-based hybrid frequent itemset mining algorithm for big data processing

Frequent itemset mining is one of the data mining techniques applied to discover frequent patterns, used in prediction, association rule mining, classification, etc. Apriori algorithm is an iterative algorithm, which is used to find frequent itemsets from transactional dataset. It scans complete dataset in each iteration to generate the large frequent itemsets of different cardinality, which seems better for small data but not feasible for big data. The MapReduce framework provides the distributed environment to run the Apriori on big transactional data. However, MapReduce is not suitable for iterative process and declines the performance. We introduce a novel algorithm named Hybrid Frequent Itemset Mining (HFIM), which utilizes the vertical layout of dataset to solve the problem of scanning the dataset in each iteration. Vertical dataset carries information to find support of each itemsets. Moreover, we also include some enhancements to reduce number of candidate itemsets. The proposed algorithm is implemented over Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation. We compare the performance of HFIM with another Spark-based implementation of Apriori algorithm for various datasets. Experimental results show that the HFIM performs better in terms of execution time and space consumption.